Regression Diagnostics for Rank-Based Methods

Residual plots and diagnostic techniques have become important tools in examining the least squares fit of a linear model. In this article we explore the properties of the residuals from a rank-based fit of the model. We present diagnostic techniques that detect outlying cases and cases that have an...

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Bibliographic Details
Published in:Journal of the American Statistical Association 1990-12, Vol.85 (412), p.1018-1028
Main Authors: McKean, Joseph W., Sheather, Simon J., Hettmansperger, Thomas P.
Format: Article
Language:English
Subjects:
Curvature
Exact sciences and technology
Least squares
Linear model
Linear models
Linear regression
Mathematical vectors
Mathematics
Nonesterified fatty acids
Outlier
Outliers
Probability and statistics
Q-Q plot
R-estimates
Regression analysis
Robust
Sciences and techniques of general use
Snakes
Standard error
Statistical variance
Statistics
Theory and Methods
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Summary:Residual plots and diagnostic techniques have become important tools in examining the least squares fit of a linear model. In this article we explore the properties of the residuals from a rank-based fit of the model. We present diagnostic techniques that detect outlying cases and cases that have an influential effect on the rank-based fit. We show that the residuals from this fit can be used to detect curvature not accounted for by the fitted model. Furthermore, our diagnostic techniques inherit the excellent efficiency properties of the rank-based fit over a wide class of error distributions, including asymmetric distributions. We illustrate these techniques with several examples.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1990.10474972